In this episode, Matthew Grant sits down with Nicola Turner and Alex Ley, co-founders of Scrub AI, to explore one of the most pressing strategic questions facing insurers today: Build vs Buy in the age of generative AI.

Five years into building an AI-driven data cleansing platform for carriers and brokers, Nicola and Alex have seen the market shift from scepticism to urgency. Boardrooms are now asking how AI is being embedded into underwriting workflows, and whether those capabilities should be developed internally or sourced from specialists.

Drawing on their experience building deterministic AI models for exposure data and catastrophe modelling, they offer a grounded perspective on what works, what breaks and where the real risks sit.

At the heart of the discussion is a simple truth: getting to 80% is easy. Getting the final 20% right is where strategy, domain expertise and long-term thinking matter most.

In this conversation, Nicola and Alex share:

Why Build vs Buy has intensified as generative AI moves from experimentation to executive priority
How investor pressure and board-level scrutiny are shaping AI strategy inside large carriers
Why generative AI can accelerate development but does not remove the complexity of insurance data
The danger of plausible but wrong outputs in exposure management and catastrophe modelling
Why deterministic AI still plays a critical role in delivering consistent, renewal-ready data
How inconsistent data cleaning can distort underwriting decisions and renewal pricing
The hidden cost of technical debt when insurers attempt to build internally
Why maintaining and iterating ai tools is often harder than building the first version

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